1. Field of the Invention
The present invention relates to a method of reconstructing a biological tissue image, and a method and apparatus for acquiring a biological tissue image, and more particularly, to a method and apparatus for acquiring a biological tissue image reconstructed from measured spectrum data of a biological tissue. The present invention also relates to an image display for clearly displaying a diseased site in pathological diagnosis through use of the thus acquired biological tissue image.
2. Description of the Related Art
Hitherto, there has been performed pathological diagnosis, that is, observing a biological tissue with a microscope or the like and diagnosing the presence or absence of a lesion and a type of the lesion based on the observation. The pathological diagnosis requires visualization of a constituent substance or contained substance correlated with a biological tissue to be observed. Heretofore, a technique for staining a specific antigen protein through use of an immunostaining method has mainly been employed in the pathological diagnosis. When breast cancer is taken as an example, an estrogen receptor (ER) (expressed in a hormone-dependent tumor, which serves as a judgment criterion for a hormone therapy), and HER2 (membrane protein found in a fast-growing malignant cancer, which serves as a judgment criterion for Herceptin administration), are visualized by the immunostaining method. However, the immunostaining method involves the following problems. Its reproducibility is poor because an antibody is unstable and antigen-antibody reaction efficiency is difficult to control. Further, in the future, for example, when there arises a need for detection of several tens or more kinds of constituent substances or contained substances, there is a problem in that the currently-employed immunostaining method cannot meet the need any more.
Further, in some cases, the visualization of the constituent substance or contained substance is required at a cellular level, not at a tissue level. For example, it was revealed that a tumor was formed in only part of fractions of a tumor tissue after xenotransplantation to immunocompromised mice. Therefore, it is understood that growth of a tumor tissue, in which cancer stem cells are recognized, depends on differentiation and self-renewal abilities of the cancer stem cells. In such research, it is necessary to observe an expression distribution of a constituent substance or contained substance in an individual cell in a tissue, not the entire tissue.
As described above, in the pathological diagnosis, a constituent substance or contained substance correlated with a tumor tissue or the like is required to be exhaustively visualized at a cellular level. There are given, as candidates of a method for the visualization, secondary ion mass spectrometry (SIMS) such as time-of-flight secondary ion mass spectrometry (TOF-SIMS) and Raman spectroscopy. In measurement by the SIMS or Raman spectroscopy, information at each point (region) in a space can be obtained with a high spatial resolution. That is, spatial distribution information on each peak value for a measured spectrum correlated with an object to be measured is obtained. Consequently, a spatial distribution of a substance in a biological tissue correlated with the measured spectrum can be determined.
The SIMS is a method involving irradiating a sample with a primary ion beam, and detecting a secondary ion emitted from the sample, thereby obtaining a mass spectrum at each point on the sample. For example, in the TOF-SIMS, through utilization of the fact that a time-of-flight of a secondary ion depends on a mass m and charge z of the ion, the secondary ion is identified, and hence a mass spectrum at each point on a sample can be obtained.
The Raman spectroscopy involves acquiring a Raman spectrum by irradiating a substance with a laser beam, which is monochromatic light, as a light source, and detecting the generated Raman scattered light with a spectrometer or an interferometer. A difference between a frequency of the Raman scattered light and a frequency of incident light (Raman shift) has a value peculiar to a structure of a substance. Hence, a Raman spectrum specific for an object to be measured can be acquired.
As used herein, the “cellular level” means a level at which at least an individual cell can be identified. A diameter of the cell falls within a range of approximately 10 μm to 20 μm (provided that a large cell such as a nerve cell has a diameter of about 50 μm). Thus, in order to acquire a two-dimensional distribution image at a cellular level, the spatial resolution needs to be 10 μm or less, and is preferably 5 μm or less, more preferably 2 μm or less, still more preferably 1 μm or less. The spatial resolution may be determined from, for example, results of linear analysis of a knife-edge sample. That is, the spatial resolution is determined based on the following general definition: “a distance between two points at which signal intensities attributed to a substance of interest near the boundary of a sample are 20% and 80%, respectively.”
Hitherto, in order to acquire biological information from the measured spectrum data, an classifier generated by machine learning in advance has been applied to the measured spectrum data of a sample (Japanese Patent Application Laid-Open No. 2010-71953). Meanwhile, a biological tissue image is essential for the pathological diagnosis, and hence an attempt has been made to display an image obtained by superimposing a measured spectrum image (spectrum information) and an optical image (morphological information) (Japanese Patent Application Laid-Open No. 2010-85219). As used herein, the machine learning refers to a technique involving empirically learning previously acquired data, and interpreting newly acquired data based on the learning results. Further, the classifier refers to judgment criterion information to be generated by empirically learning a relationship between previously acquired data and biological information.
Hitherto, one (for one point on a space or the entire sample) measured spectrum data has been used for diagnosis through application of the classifier generated by the machine learning, as described in Japanese Patent Application Laid-Open No. 2010-71953 as well. Thus, acquisition of a biological tissue image from a spatial distribution of a measured spectrum has not been envisaged. Further, although there is an example of superimposing a measured spectrum image (spectrum information) and an optical image (morphological information), there is no example of acquiring a biological tissue image involving applying machine learning (classifier) to both of the spectrum information and the morphological information. That is, there is no disclosure of a method of acquiring a biological tissue image with high accuracy involving displaying diagnosis results of, for example, the presence or absence of cancer from measurement results of spectra having a spatial distribution in a biological tissue of interest.
There is a correlation between cell morphology and pathology (e.g., a cancer cell and a normal tissue). Hence, when morphological information can also be incorporated into analysis of a measured spectrum, derivation of highly accurate results is expected to become possible.